Detecting targeted attacks with broad context detection
Cyber security is in the midst of a paradigm shift. Targeted attacks
are outmaneuvering the prevention and detection mechanisms that
companies have in place. Endpoint protection solutions are incapable of
detecting fileless attacks that are defined by behavior and the use of
legitimate OS tools, rather than by a malicious program being installed
on a machine. Detection technologies certainly detect suspicious events,
but too often they fail to filter out noise from critical incidents,
generating overwhelming numbers of alerts that have no hope of being
According to a 2017 EMA study,1 79% of security teams reported being
overwhelmed by high numbers of threat alerts. And it’s no wonder: for
example, a study by Ovum found that 37% of banks receive more than
200,000 alerts per day, and 61% receive over 100,0002. The Ponemon
Institute reports that nearly half of all security alerts are false
positives3. Of the rest, a large share is inconsequential and easily
remedied. With the possibility to examine only a tiny fraction of
alerts, overstretched security teams are forced to let the majority of
alerts triggered on a daily basis go without attention. Teams are left
frustrated. EMA found that 52% of operations personnel feel high levels
of stress, with 21% of them stating that “not enough manpower” is a
stress driver.1 The cyber security skills shortage itself is
well-documented, with a 2017 ESG/ISSA finding it worsening and impacting
70% of organizations.
Despite having cyber security high in our collective awareness, and
companies are still struggling with breaches. The average breach dwell
time is reported to be 100 days, or more depending on the industry and
study3. Companies are still being caught off-guard with breaches
exposing their networks, and their customers. All the while, the
intruders continue, concealed by a sea of alerts.
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